Video analysis of, say, a movie typically involves analyzing of a sequence of images contained in the video. The analysis is performed different points of view such as analysis for image/video compression, analysis for image/video annotation, or analysis for spam detection. As can be observed, one kind of video/image analysis is for machine processing while the other kind of video/image analysis is for providing information directly or indirectly to users. Note that video/image compression falls into the first kind while the video/image annotation is of second kind. For example, video/image annotations help in supporting semantics based end user queries on videos and relevance based ad targeting while watching the videos. For a successful annotation of an image, it is necessary to undertake the semantic analysis of the image: the image is analyzed to identify the prominent objects in the image so as provide that annotation based on these recognized objects. Note that both object recognition and identification of prominent objects are a complex and error prone processes there by leading to the not-so-very accurate image annotation. One approach to contain this complexity and enhance the accuracy of image annotation is to exploit the domain semantics during image processing.